Automatic diagonal scaling of workloads in a distributed computing environment

ABSTRACT

Embodiments for automatic diagonal scaling of workloads in a distributed computing environment. For each of a plurality of resources of each of a plurality of application instances, a determination as to whether a change in allocation of at least one of the plurality of resources is required. Operations requirements are computed for each of the plurality of application instances, the computed requirements including vertical increase and decrease operations, and horizontal split and collapse operations. The vertical decrease and horizontal collapse operations are first processed, the vertical increase and horizontal split operations are ordered, and the vertical increase and horizontal split operations are subsequently processed based on the ordering, thereby optimizing application efficiency and utilization of the plurality of resources in the distributed computing environment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is related to the following five Applications havingAttorney Docket Numbers CA820160278US01, CA820160279US01,CA820160280US01, CA820160281US01 and CA820160282US01, each filed on evendate as the present Application.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to computing systems, and moreparticularly to, various embodiments for optimizing resource usagewithin and/or between distributed computing components.

Description of the Related Art

In today's society, computer systems are commonplace. Computer systemsmay be found in the workplace, at home, or at school. As computersystems become increasingly relied upon, convenient and portable, theInternet, and the dependency thereon, has grown exponentially. Now, morethan ever before, individuals and businesses rely upon distributedcomputing systems (commonly referred to as “the cloud”) to processworkloads and store information and data. As wide strides intechnological advancement relating to workload processing, resourceallocation and data access have been accomplished, there is anever-growing demand for growth and development within the back-endsupporting systems that provide these functions.

SUMMARY OF THE INVENTION

A computer cluster, referred to as cluster for short, is a type ofcomputer system which completes computing jobs by means of multiplecollaborative computers (also known as computing resources such assoftware and/or hardware resources) which are connected together. Thesecomputing resources which are in a same management domain have a unifiedmanagement policy and provide services to users as a whole. A singlecomputer in a cluster system is usually called a node or a computingnode.

Computing clusters often implement various techniques to optimizeresources used to execute workloads performed therein as provided byusers. One common technique generally employed is the use of scalingresources and/or applications. Diagonal scaling is a recent approachthat applies a combination of vertical scaling and horizontal scaling tofit the resource requirements of each application. Vertical scalingrefers to the addition and reduction of resources for a particularrunning application instance. Horizontal scaling refers to the creationand removal of application instances. In other words, vertical scalingmay be employed to allocate or de-allocate certain resources of thecomputing cluster (e.g., memory, central processing units (CPUs),storage capacity, etc.) to an application, or rather one of many runninginstances of the application, and horizontal scaling may be employed toadd or remove one or more of the application instances of the sameapplication. Diagonal scaling combines both of these techniques toensure that computing cluster resources are appropriated and utilizedefficiently, to automatically accommodate dynamic changes in theresource requirements of applications.

Existing technologies tend to focus on horizontal scaling, namely addingand removing application instances. The embodiments described hereinfeature mechanisms for both vertical scaling (adding and removingresources for an application instance) and horizontal scaling (addingand removing application instances). In addition, the present inventionprovides a unified algorithmic mechanism that automatically applies bothvertical and horizontal scaling, creating a synergy between vertical andhorizontal scaling (i.e., diagonal scaling), to optimize the efficiencyof applications and platforms.

Many existing technologies target virtual machines or containers andrequire use of their specific properties and capabilities (e.g., formeasuring usage and defining resource requirements) to implementapplication or resource scaling, and some existing technologies requireusers to predefine a target for the utilization level of a resource(e.g., CPU) to facilitate scaling operations. The arbitrary nature ofsuch targets, and the fact that these targets refer to infrastructureresources rather than higher level quality of service aspects ofapplications, can make the behavior of such scaling mechanisms lessaligned with actual requirements for quality of service of applications.The mechanisms presented herein do not use such pre-defined targets, andinstead align the provided resources to the actual load of applicationsby considering applications' priorities and quality of service aspects.Further, the mechanisms of the present invention provide generic methodsfor implementing diagonal scaling, without relying on the form in whichan application runs.

Traditionally, existing technologies consider each resource on its ownand each application on its own, separated from other applications, indetermining scaling operations. Using the functionality of themechanisms described herein, the measurements of all relevant resourcesand the collective formation and priorities of the applications areconsidered in determining scaling operations.

Finally, some of the existing technologies require and depend on theavailability of other technologies (e.g., technologies for collectingusage metrics) for implementing scaling operations, and other existingtechnologies work with aggregated metrics for scaling purposes, withoutseparation as to specific metrics. The present invention does notnecessitate or depend on other technologies. Furthermore, in the presentinvention, measurements of all relevant metrics are appliedindividually, and scaling operations are then determined based on acollective view of metrics and applications.

In view of the existing methods known and to improve upon the art, thenew algorithms and methods considered in this disclosure providecomprehensive and efficient functionality for automatic diagonal scalingby integrating vertical scaling and horizontal scaling in a unifiedautomatic mechanism to optimize the efficiency of applications andresource platforms.

Accordingly, various embodiments are disclosed herein to implement theautomatic diagonal scaling methods as will be further described. In oneembodiment, by way of example only, for each of a plurality of resourcesof each of a plurality of application instances, a determination as towhether a change in allocation of at least one of the plurality ofresources is required. Operations requirements are computed for each ofthe plurality of application instances, the computed requirementsincluding vertical increase and decrease operations, and horizontalsplit and collapse operations. The vertical decrease and horizontalcollapse operations are first processed, the vertical increase andhorizontal split operations are ordered, and the vertical increase andhorizontal split operations are subsequently processed based on theordering, thereby optimizing application efficiency and utilization ofthe plurality of resources in the distributed computing environment.

In addition to the foregoing exemplary embodiment, various other systemand computer program product embodiments are provided and supply relatedadvantages. The foregoing summary has been provided to introduce aselection of concepts in a simplified form that are further describedbelow in the Detailed Description. This Summary is not intended toidentify key features or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in determining the scopeof the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict only typical embodiments of the invention and are nottherefore to be considered to be limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings, in which:

FIG. 1 is a block diagram depicting an exemplary computing nodeaccording to an embodiment of the present invention;

FIG. 2 is an additional block diagram depicting an exemplary cloudcomputing environment according to an embodiment of the presentinvention;

FIG. 3 is an additional block diagram depicting abstraction model layersaccording to an embodiment of the present invention;

FIG. 4 illustrates a flowchart diagram depicting a method for automaticdiagonal scaling of workloads in a distributed computing environment, inaccordance with aspects of the present invention;

FIG. 5 illustrates a combination block/flowchart diagram depicting adiagonal scaling model, in accordance with aspects of the presentinvention;

FIG. 6 illustrates an additional flowchart diagram depicting a methodfor automatic diagonal scaling of workloads in a distributed computingenvironment, in accordance with aspects of the present invention;

FIG. 7A illustrates a block diagram depicting an exemplary computationmechanism for each of a plurality of resource types, in accordance withaspects of the present invention;

FIG. 7B illustrates an additional block diagram depicting an exemplarycomputation mechanism for each of the plurality of resource types, inaccordance with aspects of the present invention;

FIG. 7C illustrates still an additional block diagram depicting anexemplary computation mechanism for each of the plurality of resourcetypes, in accordance with aspects of the present invention;

FIG. 8 illustrates a combination block/flowchart diagram depicting amethod for automatic and adaptive increase and reduction of resourcesbased on historical data, in accordance with aspects of the presentinvention;

FIG. 9 illustrates a block diagram depicting exemplary diagonal scalingincrease operations, in accordance with aspects of the presentinvention;

FIG. 10 illustrates a flowchart diagram depicting a method forprocessing a computed increase operation for a resource allocation of anapplication instance, in accordance with aspects of the presentinvention;

FIG. 11 illustrates a block diagram depicting exemplary diagonal scalingdecrease operations, in accordance with aspects of the presentinvention;

FIG. 12 illustrates a flowchart diagram depicting a diagonal scalingalgorithm, in accordance with aspects of the present invention;

FIG. 13 illustrates a block diagram depicting exemplary applicationtopologies for computing application priorities, in accordance withaspects of the present invention; and

FIG. 14 illustrates a combination block/flowchart diagram depicting amethod for automatic diagonal scaling of workloads in a distributedcomputing environment, in accordance with aspects of the presentinvention.

DETAILED DESCRIPTION OF THE DRAWINGS

As previously mentioned, computing clusters often implement varioustechniques to optimize resources used to execute workloads performedtherein as provided by users. One common technique generally employed isthe use of scaling resources and/or applications. Diagonal scaling is arecent approach that applies a combination of vertical scaling andhorizontal scaling to fit the resource requirements of each application.Vertical scaling refers to the addition and reduction of resources for aparticular running application instance. Horizontal scaling refers tothe creation and removal of application instances. In other words,vertical scaling may be employed to allocate or de-allocate certainresources of the computing cluster (e.g., memory, central processingunits (CPUs), storage capacity, etc.) to an application, or rather oneof many running instances of the application, and horizontal scaling maybe employed to add or remove one or more of the application instances ofthe same application. Diagonal scaling combines both of these techniquesto ensure that computing cluster resources are appropriated and utilizedefficiently, to automatically accommodate dynamic changes in theresource requirements of applications, however, no current solutionsexist which integrate this type of scaling automatically, efficientlynor optimally.

Accordingly, the present invention employs functionality to moreefficiently optimize and utilize resources in distributed computingenvironments by way of automatic diagonal scaling. That is, thedisclosed embodiments employ efficient mechanisms for automatic diagonalscaling, having the following exemplary specifications. First, actualresource consumption of application instances is automatically trackedand compared to allocated resources of the application instances.Second, the allocated resources and the resource limits thereof areautomatically tuned (increased/decreased) according to the comparedconsumption to allocation of these resources. More particularly, when anapplication's workload grows, the mechanisms described herein makeadditional resources available to the application. Similarly, when theworkload is reduced, the resources are decreased. Third, vertical andhorizontal scaling are used automatically, according to applicationinstance and/or host status and policies. Fourth and lastly, thefunctionality herein is customizable, efficient, and easy to configure.For example, items that can be set by users may include: maximum boundson consumption of different resources (e.g., based on historicalstatistics and cost constraints); minimal bounds on availability ofdifferent resources; triggers for determining that a scaling operationis required; and policies for integrating the vertical and horizontalscaling.

According to these specifications, the proposed mechanisms provide thefollowing benefits. First, throughput of the applications is optimized,according to actual workloads that are provided as input to theapplications, priorities of the applications, and available resources.Second, a cost of resources allocated to applications is minimalized,and cluster resource utilization is improved. Thus, because of the moreefficient utilization of cluster resources, the cluster is able toaccommodate additional workloads while reducing the cost of running suchworkloads. Third, customers utilizing the cluster pay only for resourcesactually required or used to perform their respective workloads suchthat over-payment for unused resources is avoided. In addition, cloudflexibility and its monetary charging mechanisms are improved. Fourth,application efficiency is improved and additionally the configurationthereof by removing a requirement to estimate resource allocation andscalability and manually tune the same according to workloads. Fifth andfinally, the mechanisms herein use generic automatic diagonal scalingfunctionality that can be added or implemented to a variety of workloadmanagement systems.

It should be noted that the instant disclosure, for brevity, frequentsthe language of “resources”. In an actual implementation of the presentinvention, the resources termed herein may be comprised of CPUs,graphical processing units (GPUs), memory, storage devices, networkdevices, accelerator devices, or even entire computing nodes. Indeed,any hardware and/or software resources as commonly known in the art areto be construed interchangeably with “resources” or “resource types” asdescribed herein, as one practicing the art would appreciate.Additionally, the disclosure describes “application instances” ofapplications. As one of ordinary skill in the art would recognize,application instances are meant to refer to individual occurrences of aparticular executing or running application of the same, although thenature of the application or application instances thereof may varywidely according to the particular implementation of the functionalitydisclosed herein.

Additionally, it is understood in advance that although this disclosureincludes a detailed description on cloud computing, implementation ofthe teachings recited herein are not limited to a cloud computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any other type of computingenvironment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,system memory 28 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in system memory 28 by way of example, and not limitation,as well as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

In the context of the present invention, and as one of skill in the artwill appreciate, various components depicted in FIG. 1 may be used inprocessing distributed workloads using the diagonal scalingfunctionality taught herein. For example, some of the processing anddata storage capabilities associated with mechanisms of the illustratedembodiments may take place locally via local processing components,while the same components are connected via a network to remotelylocated, distributed computing data processing and storage components toaccomplish various purposes of the present invention. Again, as will beappreciated by one of ordinary skill in the art, the presentillustration is intended to convey only a subset of what may be anentire connected network of distributed computing components thataccomplish various inventive aspects collectively.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Device layer 55 includes physical and/or virtual devices, embedded withand/or standalone electronics, sensors, actuators, and other objects toperform various tasks in a cloud computing environment 50. Each of thedevices in the device layer 55 incorporates networking capability toother functional abstraction layers such that information obtained fromthe devices may be provided thereto, and/or information from the otherabstraction layers may be provided to the devices. In one embodiment,the various devices inclusive of the device layer 55 may incorporate anetwork of entities collectively known as the “internet of things”(IoT). Such a network of entities allows for intercommunication,collection, and dissemination of data to accomplish a great variety ofpurposes, as one of ordinary skill in the art will appreciate.

Device layer 55 as shown includes sensor 52, actuator 53, “learning”thermostat 56 with integrated processing, sensor, and networkingelectronics, camera 57, controllable household outlet/receptacle 58, andcontrollable electrical switch 59 as shown. Other possible devices mayinclude, but are not limited to various additional sensor devices,networking devices, electronics devices (such as a remote controldevice), additional actuator devices, so called “smart” appliances suchas a refrigerator or washer/dryer, and a wide variety of other possibleinterconnected objects.

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provides cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, in the context of the illustratedembodiments of the present invention, various resource and applicationscaling functions 96. In addition, resource and application scalingfunctions 96 may include such operations as analyzing certain data toallocate or de-allocate resources to certain workloads and/or create orremove application instances of the certain workloads, as will befurther described. One of ordinary skill in the art will appreciate thatthe resource and application scaling functions 96 may also work inconjunction with other portions of the various abstractions layers, suchas those in hardware and software 60, virtualization 70, management 80,and other workloads 90 (such as data analytics processing 94, forexample) to accomplish the various purposes of the illustratedembodiments of the present invention.

Continuing, FIG. 4 illustrates an exemplary method 400 for automaticdiagonal scaling of workloads in a distributed computing environment, inaccordance with one embodiment of the present invention. The method 400(and the additional methods discussed hereinafter) may be performed inaccordance with the present invention in any of the environmentsdepicted in FIGS. 1-3, among others, in various embodiments. Of course,more or less operations than those specifically described in FIG. 4 maybe included in method 400, as would be understood by one of skill in theart upon reading the present descriptions.

Each of the steps of the method 400 (and the additional methodsdiscussed hereinafter) may be performed by any suitable component of theoperating environment. For example, in various embodiments, the method400 may be partially or entirely performed by a processor, or some otherdevice having one or more processors therein. The processor, e.g.,processing circuit(s), chip(s), and/or module(s) implemented in hardwareand/or software, and preferably having at least one hardware componentmay be utilized in any device to perform one or more steps of the method400. Illustrative processors include, but are not limited to, a CPU, anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), etc., combinations thereof, or any other suitablecomputing device known in the art.

The method 400 begins (step 402) by, for each of a plurality ofresources of each of a plurality of application instances, determiningas to whether a change in allocation of at least one of the plurality ofresources is required (step 404). Operations requirements are computedfor each of the plurality of application instances, the computedrequirements including vertical increase and decrease operations, andhorizontal split and collapse operations (step 406). The verticaldecrease and horizontal collapse operations are first processed (step408), the vertical increase and horizontal split operations are ordered(step 410), and the vertical increase and horizontal split operationsare subsequently processed based on the ordering (step 412), therebyoptimizing application efficiency and utilization of the plurality ofresources in the distributed computing environment. The method 400 ends(step 414).

The general approach of diagonal scaling is to scale applicationinstances vertically (allocating or de-allocating resources), and whenan application instance or a host are saturated, or when an applicationinstance is idle, to then scale horizontally (create or removeapplication instances). Subsequent to scaling horizontally, continue toscale application instances vertically. This implementation is describedin FIG. 5, which illustrates a combination block/flowchart diagramdepicting a diagonal scaling model 500.

The method 500 begins (step 502) with applying vertical scalingoperations for an application instance (step 504). That is, resourcesare either allocated or de-allocated to/from the application instance tosatisfy the resource requirements necessitated by the applicationinstance. When the application instance is either saturated (fullyutilized) or idle, or when a host is saturated, the method 500 may applyhorizontal scaling operations for the application instance (step 506).To wit, upon performing the vertical scaling of allocating orde-allocating resources in step 504, and determining that theapplication instance is still either fully saturated or idle, theapplication instance is then horizontally scaled by either adding orremoving application instances thereof. Following the horizontal scalingoperations (step 506), the method returns to applying (and continuing toapply thereinafter) vertical scaling operations if necessary (step 504).

FIG. 6 illustrates an additional flowchart diagram depicting a method600 for automatic diagonal scaling of workloads in a distributedcomputing environment, in accordance with aspects of the presentinvention. The method 600 begins by automatically tracking the resourceconsumption of each of one or more application instances and comparesthe consumption of each of the application instances to the resourceallocation of the application instance (step 602). The method 600 thencontinues by computing modification operations (increases or decreases)of resource allocations to the application instances (step 604), inaccordance with the results of the comparison in step 602. Next, thecomputed modification operations are refined according to priorities ofthe applications and/or application instances, and available resources(step 606). The computed modification operations are then dynamicallyapplied to the application instances respectively (step 608), where themodification operations are of various types, illustrated in steps 610and 612.

One type of operation which may be applied comprises increasing ordecreasing the amount of allocated resources and resource limitsallocated to a particular application instance (step 610). In this way,when an application's load grows, additional resources are madeavailable to the particular application instance. Similarly, when theload of the particular application instance is reduced, the excessresources unneeded or unused by the application instance arede-allocated from the particular application instance. Returning to step608, another type of modification operation that may be appliedcomprises adding, removing, preempting, or migrating applicationinstances (step 612). For example, if an application instance isstateless, the stateless application instance may be easily removed orpreempted. Conversely, if an application instance is stateful, theapplication instance may be better suited to be migrated to alternativeresources in the computing cluster. The handling of statefulapplications and the dependencies thereof will be discussed further,below.

Identifying Requirements for Changes in Resource Allocations

FIG. 7A illustrates a block diagram depicting an exemplary computationmechanism 700 for each of a plurality of resource types, in accordancewith aspects of the present invention. The full bar represents thecurrent allocation 702 (plus/minus all accumulated deltas) of a specificresource for a specific application instance. Two watermarks depicted ofthe computation mechanism 700 define two tiers of consumption, relativeto the current allocation. A high watermark 706 defines a low bound fora high tier 704 of consumption. If the high watermark 706 is not definedfor a specific resource, the scale up mechanism (vertical scaling ofallocating resources) is not activated for that resource.

Similarly, a low watermark 708 defines a high bound for a low tier 710of consumption. If the low watermark 708 is not defined for a specificresource, the scale down mechanism (vertical scaling of de-allocatingresources) is not activated for that resource. Sustained consumption atthe high tier 704 will trigger an increase of allocation of the specificresource for the specific application instance, and again similarly,sustained consumption at the low tier 710 will trigger a decrease ofallocation of the specific resource for the specific applicationinstance.

To define sustained consumption, a time period for qualifying for anallocation change is prescribed. This time period is a time windowhaving a sufficient number of samples remain at sustained consumptionwithin a tier (high tier 704 or low tier 710) to qualify for anallocation change. The time period may be a sliding window over time,and may have a default value. Further defined are a percentage ofoutlying samples as follows. Sustained consumption is defined, based onthese definitions, as having no more than the outlying percentage of thesamples outside of either the high tier 704 or the low tier 710 for theduration of the defined time window. For example, assuming a time windowof 1 hour and outlying percentage of 10%, if at least 90% of the samplesare in one of the tier areas in the last hour, an appropriateincrease/decrease action will be computed for the relevant resource.

Increase in Resource Allocation Handling

FIG. 7B illustrates an additional block diagram depicting an exemplarycomputation mechanism 730 for each of the plurality of resource types,in accordance with aspects of the present invention. Specifically,computation mechanism 730 illustrates a case of identified sustainedconsumption at the high tier 704.

Again, the full bar represents the current allocation 702 of a specificresource for a specific application instance. The high tier 704 is shownto include all the samples minus no more than the outlying percentage,in the inspected time window, therefore generating an increase operationfor the specific resource and the specific application instance. Theincrease operation (the additional allocation of the specific resourceto the specific application instance) may be performed with a fixedincrement, or with growing increments, or with adaptive increments, asspecified next.

An increase step 732 is defined to be an absolute value or percentage ofthe current allocation 702. Further defined is an increase functiontype, that may be configured with the following possible values: 1) onestep (default), where the allocation will be increased with one step(e.g., increase step 732); 2) growing increase (e.g., growing increase734), which is applied if the increase operations are subsequent to eachother; or 3) automatic and adaptive increase, which is based onhistorical data, as specified in the following.

A time period without an increase operation resets the growing increase734 functionality. Various functions of the growing increase 734 may beconfigured, for example, an increase value in a previous increaseoperation +1 step may be performed (e.g., where the values follow apattern of 1 step, 2 steps, 3 steps, 4 steps, 5 steps, etc.). In anotherexample, an increase value in a previous increase operation + a linearlygrowing step may be performed (e.g., where the values follow a patternof 1 step, 3 steps, 6 steps, 10 steps, 15 steps, 21 steps, etc.).

In still another example, an increase value in a previous operation x 2steps may be performed (e.g., where the values follow a pattern of 1step, 2 steps, 4 steps, 8 steps, 16 steps, 32 steps, etc.). A limit onincrease 736 is further defined to enable the user to control themaximal consumption and associated costs. In some embodiments, there maybe multiple limits on increase that are mapped to different time periods(e.g., of day), which may be useful if the cost of resources variesdepending on the time the resources are allocated and/or used. Increaseoperations computed for a resource and an application instance will notexceed the defined value (or values) of the limit on increase 736 forthat resource and application instance. Each pair of resource andapplication is further associated with an indication, which may be namedas a critical resource for increase indication. If this criticalresource for increase indication is set to be true, and the limit onincrease 736 for the resource and application instance has already beenreached while still attaining high tier 704 consumption, then anappropriate action will be taken, where the action is defined for theentire application instance and may include horizontal scaling of theapplication instance (e.g., addition of application instances).

Reduction in Resource Allocation Handling

FIG. 7C illustrates an additional block diagram depicting an exemplarycomputation mechanism 760 for each of the plurality of resource types,in accordance with aspects of the present invention. Specifically,computation mechanism 760 illustrates a case of identified sustainedconsumption at the low tier 710.

Still again, the full bar represents the current allocation 702 of aspecific resource for a specific application instance. The low tier 710is shown to include all the samples minus no more than the outlyingpercentage, in the inspected time window, therefore generating areduction operation for the specific resource and the specificapplication instance. The reduction operation (the de-allocation of thespecific resource from the specific application instance) may beperformed, with a fixed decrement, or with a maximal decrement, or withgrowing decrements, or with adaptive decrements, as specified next.

A reduction step 762 is defined to be an absolute value or percentage ofthe current allocation 702. Further defined is a reduction functiontype, that may be configured with the following possible values: 1) onestep (default), where the allocation will be reduced with one step(e.g., reduction step 762); 2) maximal reduction, where the reduction inthe allocation will be applied down to the upper step bounding a limiton reduction 766; 3) growing reduction (e.g., growing reduction 764),which is applied if the reduction operations are subsequent to eachother; or 4) automatic and adaptive reduction, which is based onhistorical data, as specified in the following.

A time period without a reduction operation resets the growing reduction764 functionality. Various functions of the growing reduction 764 may beconfigured, for example, a reduction value in a previous reductionoperation +1 step may be performed (e.g., where the values follow apattern of 1 step, 2 steps, 3 steps, 4 steps, 5 steps, etc.). In anotherexample, a reduction value in a previous reduction operation + alinearly growing step may be performed (e.g., where the values follow apattern of 1 step, 3 steps, 6 steps, 10 steps, 15 steps, 21 steps,etc.).

In still another example, a reduction value in a previous operation ×2steps may be performed (e.g., where the values follow a pattern of 1step, 2 steps, 4 steps, 8 steps, 16 steps, 32 steps, etc.). The limit onreduction 766 is further defined to enable the user to control theminimal allocation of the particular resource for the applicationinstance. In some embodiments, there may be multiple limits on reductionthat are mapped to different time periods (e.g., of day), which may beuseful if the cost of resources varies depending on the time of day theresources are allocated and/or used. Reduction operations computed for aresource and an application instance will not reduce the allocationbelow the defined value (or values) of the limit on reduction 766 forthat resource and application instance. Each pair of resource andapplication is further associated with an indication, which may be namedas a critical resource for reduction indication. If this criticalresource for reduction indication is set to be true, and the limit onreduction 766 for the resource and application instance has already beenreached while still attaining low tier 710 consumption, then anappropriate action will be taken, where the action is defined for theentire application instance and may include horizontal scaling of theapplication instance (e.g., removal of the application instance).

Automatic and Adaptive Increase and Reduction Based on Historical Data

FIG. 8 illustrates a combination block/flowchart diagram depicting amethod 800 for automatic and adaptive increase and reduction ofresources based on historical data, in accordance with aspects of thepresent invention. Beginning at step 802, the increase or reductionfunction type can be configured to be automatic and adaptive and basedon historical data 806 held by the computing cluster. To implement thisfunctionality, the mechanisms herein maintain historical consumptiondata 806 on the consumption level of each resource for each applicationacross time (step 804). When an increase or reduction operation istriggered for an application instance (step 808), a predictedconsumption level is computed based on the historical consumption data806 for the specific resource, application, and time (although there maybe additional attributes maintained and taken into consideration in thehistorical consumption data 806) (step 810). Then an increase orreduction operation is computed accordingly, to accommodate thepredicted consumption level of the resource for the application (step812). The method 800 ends (step 814).

Application Level Mechanism Increase Method

FIG. 9 illustrates a block diagram 900 depicting exemplary diagonalscaling increase operations, in accordance with aspects of the presentinvention. The operations start with a base number of instances of anapplication (902), where the base number may also be one applicationinstance. Each of the application instances 902 are optionally definedwith vertical scaling, and each application instance is computed forscaling operations independently. An additional option is to define anumber of application instances under which only horizontal scaling willbe performed, and above which, vertical scaling followed by horizontalscaling, will be performed. If this defined number of applicationinstances equals the base number of application instances 902, thenvertical scaling is applied first, followed by horizontal scaling. Thisscenario can be useful, for example, for stateful applications (havingstate data). Further, if this defined number of application instances isinfinite, then vertical scaling is practically disabled, and onlyhorizontal scaling operations will be performed. This scenario can beuseful, for example, for stateless applications (having no state data).In all cases, however, the functionality of the described embodimentsapplies automatic tracking and computation of scaling requirements.

The operations automatically track the resource consumption of eachapplication instance of the application instances (902), and compare theresource consumption of each application instance to the resourceallocation thereof In cases where an application instance's load grows,increase operations may be computed for allocation of resources assignedto a particular application instance. Following are several caseexamples for processing of a computed increase operation for a resourceallocation of an application instance:

Case 1—Resource is available: If the vertical increase can beaccommodated on the host (while considering resource availability andapplication priorities) and the application instance limit on increase736 has not been reached, then a vertical increase operation is appliedto the application instance (block 904), and vertical increaseoperations are subsequently continually applied on application instanceswhich have not crossed the threshold of the limit on increase 736 (block906) (note the application instances are represented as circles or ovalswhich grow larger during the vertical increase operations therebydenoting an amount of resources allocated to each respective applicationinstance).

Case 2—Application instance limit on increase 736 has been reached: Ifthe limit on increase 736 of the application instance for the resourcehas been reached, and the resource is configured as a critical resourcefor increase (block 908), then the application instance may be scaledfurther horizontally (block 910) with a defined number of createdapplication instances 912 of the application. The additional applicationinstances may inherit the current allocation of the particular resourceof the saturated instance or receive a new base allocation of theresource.

Case 3—Host increase limit has been reached: If the resource is fullyexhausted (while additionally considering application priorities) on thehost, one or more predefined options may be performed as follows.

A first option comprises performing horizontal scaling as discussedabove, by creating additional instances of the application on otherhosts (such as shown in created application instances 912). This optionis appropriate for stateless applications as state data is not requiredto create and/or make available for the additional application instances912 on the other, or alternative, hosts.

A second option, which may be performed additionally or alternatively tothe first option, comprises attempting to get allocation of the resourcefrom other application instances. In this scenario, the lowest priorityand least loaded application instances are computed, and requests aretransmitted to these application instances to relinquish allocation ofthe resource. If, upon the application instances relinquishingallocation of the resource to the application instance in need of theresource fails to provide sufficient allocation of the resource, theapplication instance having the resource need may be migrated to anotherhost where the resource is available for allocation. Further, if thismigration is not possible, horizontal scaling may subsequently beperformed as specified above, by creating additional applicationinstances of the application on the other hosts. This option isappropriate for stateful applications (having state data). Migration maybe preferred over horizontal scaling for stateful applications becausesuch applications may not support distribution/synchronization withregards to their state. Application data may also be migrated with anapplication instance.

FIG. 10 illustrates a flowchart diagram depicting a method 1000 forprocessing a computed increase operation for a resource allocation of anapplication instance as described in the case scenarios previously, inaccordance with aspects of the present invention. The method 1000 begins(step 1002) by receiving a computed increase operation for a resourceallocation of an application instance, that must be performed due to oneor more specific resource requirements necessitated by the applicationinstance's workload (step 1004).

At step 1006, a determination is made as to whether a vertical increase(allocation of additional resources) can be accommodated on the host andthe limit on increase 736 for the application instance has been reached.If the vertical increase can be accommodated on the host and the limiton increase 736 for the application instance has not been reached, themethod 1000 proceeds to apply a vertical increase operation to theapplication instance by allocating additional resource(s) to theapplication instance (step 1014), and the method 1000 ends (step 1026).

Returning to step 1006, if the vertical increase cannot be accommodatedon the host and/or the limit on increase 736 for the applicationinstance has been reached, a further determination is made as to whetherthe limit on increase 736 for the application instance has indeed beenreached, and if so, whether the resource is configured as a criticalresource for increase (step 1008). If the limit on increase 736 for theapplication instance has been reached and the resource is configured asa critical resource for increase, the method 1000 proceeds to scale theapplication instance horizontally by adding or creating a defined numberof additional application instances (step 1016), and the method 1000ends (step 1026).

Returning to step 1008, if the limit on increase 736 for the applicationinstance has not been reached yet, or the resource is not configured asa critical resource for increase, a further determination is made as towhether the resource is exhausted on the host while consideringpriorities of the applications (step 1010). If, at step 1010, theresource is not exhausted on the host while considering priorities ofthe applications, the method 1000 ends (step 1026).

Returning to step 1010, if the resource is exhausted on the host whileconsidering priorities of the applications, a further determination ismade as to whether the application is stateless (step 1012). If, at step1012, the application is determined to be stateless, the method 1000continues to scale the application instance horizontally by creating andadding a defined number of additional application instances to handlethe workload thereof (step 1016), and the method 1000 ends (step 1026).

Returning to step 1012, if the application is determined to be stateful,the method 1000 continues to compute the lowest priority and leastloaded application instances on the host and transmits a request forthese (one or more) instance(s) to relinquish allocation of the neededresource to the application instance requiring the resource (step 1018).Upon completing this step, a determination is made as to whether theapplication instance resource requirement was satisfied by there-allocation of the resource to the application instance necessitatingthe resource (step 1020). If, at step 1020, the resource requirement ofthe application instance was satisfied, the method 1000 ends (step1026).

Returning to step 1020, if the resource requirement was not satisfied bythe re-allocation of the resource to the application instancenecessitating the resource, and/or no resources were able to bere-allocated to the application instance necessitating the resource bythe other (alternative) application instance(s), an attempt is made tomigrate the application instance necessitating the resource to anotherhost where the resource is available for allocation (step 1022).Finally, a determination is made as to whether this migration waspossible, and if so, successful at step 1024. If the migration was notpossible and/or was not successful, the method 1000 returns to scalingthe application instance horizontally with the defined number of createdapplication instances (which may be on the same or other hosts as thebase application instance), and the method 1000 ends (step 1026).Otherwise, at step 1024, if the migration was possible and was indeedsuccessful, the method ends (step 1026).

To model the previously described scenarios and further encapsulate suchinto a user configured policy, an action for vertical increase limitreached or resource exhausted is defined. This action is performed whenat least one critical resource for increase has reached its limit onincrease 736 for the application instance or has been exhausted on thehost executing the application instance, and high tier 704 consumptioncontinues to be detected for the particular application instance. Theaction may be configured by the user with the following options: 1) tonotify the user; 2) attempt to acquire resource allocation of the neededresource from other application instances; 3) migrate the applicationinstance (to other or alternative host(s)); or 4) perform a horizontalincrease. If the user specifies option 4 (to perform a horizontalincrease operation), the user may additionally configure a number ofadditional application instances to create and what resource allocation(what amount of what resources) to apply for the additional applicationinstances. This specified resource allocation may comprise a currentallocation of the current application instance, or the newly createdapplication instances may be configured to receive a new, baseallocation of each specified resource.

Reduction Method

The objective of the reduction method is to release excessive resourcesfrom application instances, such that these resources may be reused(e.g., for other application instances necessitating the resource(s)). Ageneral approach to this reduction may be defined as to first applyvertical reduction operations, and subsequently apply horizontalreduction operations, where vertical reduction operations are appliedper resource, according to the automatic tracking of resourceconsumption techniques previously described.

To perform the horizontal reduction operations, an idle applicationinstance is defined by means of the following two possible definitions:

-   1. System defined idle instance, wherein all the critical resources    for reduction have reached their limit on reduction 766, and the    consumption levels thereof have been detected at their respective    low tier 710 for the time period for horizontal reduction    operations.-   2. User defined idle instance, wherein an interface is defined to    run a user provided procedure/executable program to determine if a    specific application instance is idle. This type of user logic is    generally effective to check the load of an application instance.

These two aforementioned techniques may work in conjunction with oneanother. For example, when an idle application instance is identifiedbased on the system definition, a user provided procedure mayadditionally be activated to verify the status of the applicationinstance. Thus, when an idle application instance is identified, inaccordance with the previously discussed techniques, the idle instancemay be terminated and its allocated resources may be released to bereused for other application instances. Furthermore, the user mayadditionally configure a minimal number of instances to be retained foran application, and if such a minimal number of application instances isconfigured, an idle application instance will be terminated only if thecurrent number of instances of the application is higher than theconfigured minimal number of instances for the application.

FIG. 11 illustrates a block diagram 1100 depicting exemplary diagonalscaling decrease operations, in accordance with aspects of the presentinvention. The operations start with a current number and formation ofinstances of an application (current number of application instances1102). That is, the operations begin by determining the current numberof application instances 1102 and the amount of resources allocated toeach respective instance (again denoted in diagram 1100 by how large theoval representing each application instance is). The resourceconsumption of each of the current application instances 1102 ismonitored and tracked, and ultimately compared to the resourceallocation of the application instances (again noting that each resourceis individually monitored and tracked for each respective applicationinstance). In cases where a particular application instance's load isreduced, vertical reduction operations may then be computed for reducingthe amounts of resources allocated and assigned to the applicationinstance (block 1104). Moreover, idle application instances may beidentified using the methods previously discussed (idle instances 1106)while the rest of the application instances may be identified as active(active instances 1108). Based on this determination of idle applicationinstances 1106, some or all of the identified idle application instances1106 may be terminated. That is, horizontal reduction operations areperformed to reduce or terminate the idle application instances 1106(block 1110).

To enable the user to configure the above techniques for determining anapplication instance as idle, a horizontal reduction action indicationis defined. If this indication is set to true for an applicationinstance, and if for the particular application instance all thecritical resources for reduction reached their limit on reduction 766,and their consumption levels for the particular application instance aredetected to be sustained at the low tier 710 for a time periodqualifying for horizontal reduction, then horizontal reduction actionwill be taken.

The horizontal reduction action can be configured by the user with thefollowing options: 1) notify the user; or 2) terminate the applicationinstance (gracefully or forcibly). Further defined by the user is thetime period for horizontal reduction, which comprises the minimalduration of time in which the conditions of idleness should be satisfiedin order for an application instance to be determined as idle.

Cluster Level Mechanism Diagonal Scaling Algorithm

FIG. 12 illustrates a flowchart diagram 1200 depicting a high-leveldiagonal scaling algorithm, in accordance with aspects of the presentinvention. When considering the diagram 1200, the achieved objectives ofthe diagonal scaling algorithm are to both maximize throughput of theapplications of the computing cluster, according to actual workload thatis provided as input to the applications, priorities of theapplications, and available resources; and minimize the cost of theseresources. To achieve these objectives, the diagonal scaling algorithmcomputes required modifications of resource allocations based on actualconsumption of specific resources per application instance, andsubsequently computes actual operations according to applicationpriorities and available resources.

The diagonal scaling algorithm begins with a first phase of computingresource requirements (phase 1202). This phase includes two steps, wherethe first (step 1204) focuses on the level of an individual resource ofan individual application instance. In step 1204, the algorithmdetermines for each individual resource of an application instance if achange in allocation of the resource is required, using the methods asdescribed previously. The second step in phase 1202 (step 1206) focuseson the application instance level. In step 1206, the algorithm computesthe operations requirements for each application instance, using themethods as previously described. These computed operations requirementsinclude both vertical and horizontal operations, where again, verticalscaling operations increase and decrease the allocation for individualresources per application instance, and horizontal scaling operationssplit or collapse instances of an application. Splitting an applicationinstance denotes creating additional application instances, andcollapsing an application instance denotes removing that applicationinstance.

The diagonal scaling algorithm then proceeds to the second phase (phase1208) of processing the requirements that were computed in the previousstep. Phase 1208 includes two steps. The first step in phase 1208 (step1210) focuses on the level of an application. That is, in step 1210, thealgorithm processes the vertical decrease and horizontal collapseoperations computed in step 1206, using the methods as previouslydescribed. The second step in phase 1208 (step 1212) focuses on thecluster level. Thus, in step 1212, the algorithm computes or obtainspriorities for the applications and orders the vertical increase andhorizontal split operations based on the priorities of the applications(e.g., using a priority queue). Prioritizing and ordering will befurther discussed, following. Further in step 1212, the algorithm thenprocesses the vertical increase and horizontal split operations based onthe ordering, congruent with the description of FIGS. 9-11 examinedpreviously.

Computing Priorities of Applications for Scaling Operations

FIG. 13 illustrates a block diagram 1300 depicting exemplary applicationtopologies for computing application priorities, in accordance withaspects of the present invention. Consider the applications topologyshown in diagram 1300, where four applications are shown, namelyapplication(1) 1302, application(2) 1304, application(3) 1306, andapplication(4) 1308. For each of the applications 1302-1308, arespective “S[application]” is defined therein as the significance (orimportance) of the respective applications 1302-1308 or thefunctionality performed thereof. That is, the significanceS[application1] of application(1) 1302 may have a higher importance (theapplication itself and/or the functionality performed by application(1)1302) than the significance S[application2] of application(2) 1304, andso on. A scale for this application significance may comprise 5exemplary levels, however these levels may be modified as requireddepending on implementation. In diagram 1300, each of the applications1302-1308 is associated with its respective S[application] value.

Consider application(1) 1302 in diagram 1300. For application(1) 1302,the diagram 1300 illustrates an example for application dependencies,such that, in this example, application(2) 1304, application(3) 1306,and application(4) 1308 each hold dependencies on application(1) 1302,and additionally, application(1) 1302 holds dependencies on otherapplications (not shown). Assuming without loss of generality that alldependencies on application(1) 1302 are shown in this example, and foreach dependency, S[dependency] (or S[dpn]) is defined as thesignificance (or importance) of the dependency of the dependentapplication on the antecedent application.

Further illustrated in diagram 1300, each of the dependencies betweenapplication(2) 1304, application(3) 1306, application(4) 1308 andapplication(1) 1302 is associated with a respective “S[dpn]” value. Toillustrate the difference between the two types of significance,consider the following two examples: (1) Two important applications,where one application has a usage (or dependency) of the other yet wherethis usage is for a low importance functionality; and (2) Two lowsignificance applications, where one application uses the other suchthat this usage facilitates the main functionality of the dependentapplication, hence having a high significance value for this dependency.

The user provides significance values for each application anddependency thereof. The diagonal scaling algorithm standardizes thesesignificance values, computes priorities for the applications, andrecomputes the priorities when the dependency topology is changed orwhen applications are added or removed from the cluster. Following is aformula by which the algorithm computes the priority of an applicationX:

$\begin{matrix}{{{Priority}\mspace{14mu}\left\lbrack {{application}\mspace{14mu} X} \right\rbrack} =} & \left. A \right) \\{W \times {S\left\lbrack {{applicaiton}\mspace{14mu} X} \right\rbrack}\left( {1 - W} \right) \times \frac{\begin{matrix}\sum\limits_{\lbrack{{{application}\mspace{14mu} Y} \in {{applications}\mspace{14mu} {dependent}\mspace{14mu} {on}\mspace{14mu} {application}\mspace{14mu} X}}\rbrack} \\\left\lbrack {{S\left\lbrack {{{dependency}\mspace{14mu} Y}->X} \right\rbrack} \times {S\left\lbrack {{application}\mspace{14mu} Y} \right\rbrack}} \right\rbrack\end{matrix}}{\lbrack{Divisor}\rbrack}} & \left. B \right)\end{matrix}$

In this formula, the first element (A) models the significance of theapplication and the second element (B) models the significance of thedependencies on the application. W is a relative weight of theapplication significance versus the significance of the dependencies onthe application. An example range for the values of W may be 0 to 1, andW may have a default value. The significance values S [application ordependency] may also be standardized to a value range of 0 to 1.

To be more clear and as shown in formula (elements A and B) above, thepriority of an application may be computed by 1) computing the firstelement as a product of the significance of the application and a firstweight; 2) computing the second element by summarizing the products ofthe significance of the dependencies on the application and thesignificance of the dependent applications, dividing the summary with adivisor, and multiplying the result with a second weight; and 3) addingthe first element and the second element.

The Divisor in the second element may be defined in several ways.Following are example embodiments for computing the Divisor:

Divisor = Total  number  of  applications − 1${Divisor} = {\underset{\lbrack{{{application}\mspace{14mu} I} \in {{all}\mspace{14mu} {applications}}}\rbrack}{MAX}\left\lbrack {\sum\limits_{\lbrack{{{application}\mspace{14mu} J} \in {{applications}\mspace{14mu} {dependent}\mspace{14mu} {on}\mspace{14mu} {application}\mspace{14mu} I}}\rbrack}1} \right\rbrack}$${Divisor} = {\underset{\lbrack{{{application}\mspace{14mu} I} \in {{all}\mspace{14mu} {applications}}}\rbrack}{MAX}{\quad{{\left\lbrack {\sum\limits_{\lbrack{{{application}\mspace{14mu} J} \in {{applications}\mspace{14mu} {dependent}\mspace{14mu} {on}\mspace{14mu} {application}\mspace{14mu} I}}\rbrack}{S\left\lbrack {{application}\mspace{14mu} J} \right\rbrack}} \right\rbrack {Divisor}} = {\underset{\lbrack{{{application}\mspace{14mu} I} \in {{all}\mspace{14mu} {applications}}}\rbrack}{MAX}{\quad\left\lbrack {\sum\limits_{\lbrack{{{application}\mspace{14mu} J} \in {{applications}\mspace{14mu} {dependent}\mspace{14mu} {on}\mspace{14mu} {application}\mspace{14mu} I}}\rbrack}{S\left. \quad{\left\lbrack {{{dependency}\mspace{14mu} J}->I} \right\rbrack \times {S\left\lbrack {{application}\mspace{14mu} J} \right\rbrack}} \right\rbrack}} \right.}}}}}$

That is, the Divisor may be computed as 1) the total number ofapplications minus one, as in the first example; 2) the maximal numberof dependencies on a given application from among all the applications;3) the maximal sum of significance values of applications that aredependent on an application, among all applications; and/or 4) themaximal sum of the products of the significance values of applicationsthat are dependent on an application with the significance values of thedependencies, among all applications.

It should be noted that if the significance values and W are both in therange of 0 to 1, then the prescribed formula for computing the priorityof an application generates a value in the range of 0 to 1 for the givenapplication.

The given algorithm has the following distinctive features: (1) Thealgorithm considers (i.e., uses as input) the significance of eachdependency, while existing methods generally do not associate anysignificance value to the dependencies themselves; (2) The algorithmcombines an application's significance with its respective dependency'ssignificance, while existing methods typically do not associate anelement to be ranked with inherent or user perceived significance; and(3) The algorithm enables a non-iterative computation of theapplications' priorities such that recomputation is required only whenthere is change in the topology of applications and their dependencies,or a change in the significance values. Existing methods typically useiterative computations, because the input is different and the type andscale of topology is different. Thus, the provided algorithm addresses atopology and scale that enable to perform an efficient non-iterativecomputation.

Example System Embodiment

FIG. 14 illustrates a combination block/flowchart diagram 1400 depictinga system embodiment for automatic diagonal scaling of workloads in adistributed computing environment, in accordance with aspects of thepresent invention.

The inputs to the system are applications specifications (block 1402)and applications monitoring information (block 1404). A scalingcomputations component (block 1406) uses the applications specificationsand priorities as discussed above, combined with the monitoringinformation of block 1404, to compute scaling operations, which may beperformed concurrently. The scaling computations component 1406 thenadds the computed scaling operations as tasks to operations executionqueues.

Two queues are defined: One queue for parallel operations (block 1408)(i.e., release operations of resources) and a second queue forprioritized operations (block 1410) (i.e., allocation operations ofresources). A scaling operations component (block 1412) obtains tasksfrom the operations execution queues 1408 and 1410, and executes thetasks. The scaling operations component 1412 may also perform anycombination of the following: (1) Compute and apply appropriate resourceallocation and release operations using a system scheduler or resourcemanager (block 1414); (2) Set or modify resource consumption limits forapplication instances running on specific hosts (block 1416); (3) Adjustthe configuration of the application instances to the updated resourcesavailable for the application instances (e.g., increase/decrease thenumber of threads within an application instance) (block 1418); and/or(4) Create and remove application instances (block 1420). It should benoted that the scaling operations component may be a distributedcomponent, or use a distributed mechanism, and may apply independentoperations concurrently.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowcharts and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowcharts and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowcharts and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowcharts or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustrations, and combinations ofblocks in the block diagrams and/or flowchart illustrations, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts or carry out combinations of special purposehardware and computer instructions.

1. A method for automatic diagonal scaling of workloads in a distributedcomputing environment, by at least one processor, comprising:determining, for each of a plurality of resources of each of a pluralityof application instances, whether a change in allocation of at least oneof the plurality of resources is required; computing operationsrequirements for each of the plurality of application instances, thecomputed requirements including vertical increase and decreaseoperations, and horizontal split and collapse operations; processing thevertical decrease and horizontal collapse operations; ordering thevertical increase and horizontal split operations; and processing thevertical increase and horizontal split operations based on the ordering,thereby optimizing application efficiency and utilization of theplurality of resources in the distributed computing environment.
 2. Themethod of claim 1, wherein the determining is performed by comparingactual consumption of each of the plurality of resources to a currentallocation of each of the plurality of resources.
 3. The method of claim1, wherein: the vertical increase and decrease operations includeincreasing and decreasing allocation of resources for applicationinstances, respectively; the horizontal split and collapse operationsinclude creating and removing instances of an application, respectively;and the ordering is based on priorities of applications having theplurality of application instances.
 4. The method of claim 3, furtherincluding configuring a scaling operations component by performing atleast one of: receiving as input, specifications and priorities of theapplications, and monitoring information of application instancesthereof; computing scaling operations by combining the inputinformation; and adding the computed scaling operations as tasks tooperations execution queues; wherein the computation of scalingoperations is performed for each of the plurality of applicationinstances concurrently.
 5. The method of claim 4, further includingperforming at least one of: configuring a first one of the operationsexecution queues for release operations of resources, the releaseoperations performed in parallel; and configuring a second one of theoperations execution queues for allocation operations of resources, theallocation operations performed according to the ordering of thepriorities.
 6. The method of claim 4, further including configuring thescaling operations component by: receiving as input a plurality of thetasks from the operations execution queues; and performing each one ofthe plurality of tasks, wherein the performing includes at least one of:computing and applying the resource allocation and release operationsusing a system scheduler or resource manager; setting or modifyingresource consumption limits for application instances of the pluralityof application instances running on specific hosts; adjusting theapplication instances running on the specific hosts to the updatedresources available; and creating and removing the application instanceson the specific hosts.
 7. The method of claim 6, wherein the ordering ofthe operations performed by the scaling operations component for eachtask consisting of increasing the resource allocation includes: firstcomputing and applying the resource allocation operations, subsequentlysetting and modifying the resource consumption limits, and subsequentlyadjusting respective application instances to updated resourcesavailable.
 8. The method of claim 7, wherein the ordering of theoperations performed by the scaling operations component for each taskconsisting of decreasing the resource allocation includes: firstadjusting the respective application instances to the updated resourcesavailable, subsequently setting and modifying the resource consumptionlimits, and subsequently computing and applying the resource releaseoperations.
 9. A system for automatic diagonal scaling of workloads in adistributed computing environment, the system comprising: at least oneprocessor executing instructions stored in a memory, wherein theprocessor device, when executing the instructions: determines, for eachof a plurality of resources of each of a plurality of applicationinstances, whether a change in allocation of at least one of theplurality of resources is required; computes operations requirements foreach of the plurality of application instances, the computedrequirements including vertical increase and decrease operations, andhorizontal split and collapse operations; processes the verticaldecrease and horizontal collapse operations; orders the verticalincrease and horizontal split operations; and processes the verticalincrease and horizontal split operations based on the ordering, therebyoptimizing application efficiency and utilization of the plurality ofresources in the distributed computing environment.
 10. The system ofclaim 9, wherein the determining is performed by comparing actualconsumption of each of the plurality of resources to a currentallocation of each of the plurality of resources.
 11. The system ofclaim 9, wherein: the vertical increase and decrease operations includeincreasing and decreasing allocation of resources for applicationinstances, respectively; the horizontal split and collapse operationsinclude creating and removing instances of an application, respectively;and the ordering is based on priorities of applications having theplurality of application instances.
 12. The system of claim 11, whereinthe at least one processor configures a scaling operations component byperforming at least one of: receiving as input, specifications andpriorities of the applications, and monitoring information ofapplication instances thereof; computing scaling operations by combiningthe input information; and adding the computed scaling operations astasks to operations execution queues; wherein the computation of scalingoperations is performed for each of the plurality of applicationinstances concurrently.
 13. The system of claim 12, wherein the at leastone processor performs at least one of: configuring a first one of theoperations execution queues for release operations of resources, therelease operations performed in parallel; and configuring a second oneof the operations execution queues for allocation operations ofresources, the allocation operations performed according to the orderingof the priorities.
 14. The system of claim 12, wherein the at least oneprocessor configures the scaling operations component by: receiving asinput a plurality of the tasks from the operations execution queues; andperforming each one of the plurality of tasks, wherein the performingincludes at least one of: computing and applying the resource allocationand release operations using a system scheduler or resource manager;setting or modifying resource consumption limits for applicationinstances of the plurality of application instances running on specifichosts; adjusting the application instances running on the specific hoststo the updated resources available; and creating and removing theapplication instances on the specific hosts.
 15. The system of claim 14,wherein the ordering of the operations performed by the scalingoperations component for each task consisting of increasing the resourceallocation includes: first computing and applying the resourceallocation operations, subsequently setting and modifying the resourceconsumption limits, and subsequently adjusting respective applicationinstances to updated resources available.
 16. The system of claim 15,wherein the ordering of the operations performed by the scalingoperations component for each task consisting of decreasing the resourceallocation includes: first adjusting the respective applicationinstances to the updated resources available, subsequently setting andmodifying the resource consumption limits, and subsequently computingand applying the resource release operations.
 17. A computer programproduct for automatic diagonal scaling of workloads in a distributedcomputing environment by at least one processor, the computer programproduct embodied on a non-transitory computer-readable storage mediumhaving computer-readable program code portions stored therein, thecomputer-readable program code portions comprising: an executableportion that determines, for each of a plurality of resources of each ofa plurality of application instances, whether a change in allocation ofat least one of the plurality of resources is required; an executableportion that computes operations requirements for each of the pluralityof application instances, the computed requirements including verticalincrease and decrease operations, and horizontal split and collapseoperations; an executable portion that processes the vertical decreaseand horizontal collapse operations; an executable portion that ordersthe vertical increase and horizontal split operations; and an executableportion that processes the vertical increase and horizontal splitoperations based on the ordering, thereby optimizing applicationefficiency and utilization of the plurality of resources in thedistributed computing environment.
 18. The computer program product ofclaim 17, wherein the determining is performed by comparing actualconsumption of each of the plurality of resources to a currentallocation of each of the plurality of resources.
 19. The computerprogram product of claim 17, wherein: the vertical increase and decreaseoperations include increasing and decreasing allocation of resources forapplication instances, respectively; the horizontal split and collapseoperations include creating and removing instances of an application,respectively; and the ordering is based on priorities of applicationshaving the plurality of application instances.
 20. The computer programproduct of claim 19, further including an executable portion thatconfigures a scaling operations component by performing at least one of:receiving as input, specifications and priorities of the applications,and monitoring information of application instances thereof; computingscaling operations by combining the input information; and adding thecomputed scaling operations as tasks to operations execution queues;wherein the computation of scaling operations is performed for each ofthe plurality of application instances concurrently.
 21. The computerprogram product of claim 20, further including an executable portionthat performs at least one of: configuring a first one of the operationsexecution queues for release operations of resources, the releaseoperations performed in parallel; and configuring a second one of theoperations execution queues for allocation operations of resources, theallocation operations performed according to the ordering of thepriorities.
 22. The computer program product of claim 20, furtherincluding an executable portion that configures the scaling operationscomponent by: receiving as input a plurality of the tasks from theoperations execution queues; and performing each one of the plurality oftasks, wherein the performing includes at least one of: computing andapplying the resource allocation and release operations using a systemscheduler or resource manager; setting or modifying resource consumptionlimits for application instances of the plurality of applicationinstances running on specific hosts; adjusting the application instancesrunning on the specific hosts to the updated resources available; andcreating and removing the application instances on the specific hosts.23. The computer program product of claim 22, wherein the ordering ofthe operations performed by the scaling operations component for eachtask consisting of increasing the resource allocation includes: firstcomputing and applying the resource allocation operations, subsequentlysetting and modifying the resource consumption limits, and subsequentlyadjusting respective application instances to updated resourcesavailable.
 24. The computer program product of claim 23, wherein theordering of the operations performed by the scaling operations componentfor each task consisting of decreasing the resource allocation includes:first adjusting the respective application instances to the updatedresources available, subsequently setting and modifying the resourceconsumption limits, and subsequently computing and applying the resourcerelease operations.